484 citations · 1.3k across the 19 of their papers we have counts for
4 papers · 1 filter
Multi-view biomedical foundation models for molecule-target and property prediction
Parthasarathy Suryanarayanan, Yunguang Qiu, Shreyans Sethi +15
Quality molecular representations are key to foundation model development in bio-medical research. Previous efforts have typically focused on a single representation or molecular v…
In-Pocket 3D Graphs Enhance Ligand-Target Compatibility in Generative Small-Molecule Creation
Seung-gu Kang, Jeffrey K. Weber, Joseph A. Morrone +3
Proteins in complex with small molecule ligands represent the core of structure-based drug discovery. However, three-dimensional representations are absent from most deep-learning-…
Analysis of training and seed bias in small molecules generated with a conditional graph-based variational autoencoder -- Insights for practical AI-driven molecule generation
Seung-gu Kang, Joseph A. Morrone, Jeffrey K. Weber +1
The application of deep learning to generative molecule design has shown early promise for accelerating lead series development. However, questions remain concerning how factors li…
Combining docking pose rank and structure with deep learning improves protein-ligand binding mode prediction
Joseph A. Morrone, Jeffrey K. Weber, Tien Huynh +2
We present a simple, modular graph-based convolutional neural network that takes structural information from protein-ligand complexes as input to generate models for activity and b…